Biosignal Classification and Disease Prediction with Deep Learning

Alan, Murat; Aküner, Mustafa Caner; Kepez, Alper

doi:10.1109/asyu50717.2020.9259852

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…We design two network models, one for the prediction of drain transient current pulse and the other for the prediction of drain transient current peak and the total collected charge. In the process of network training and network testing, we use such parameters as accuracy, mean square error, and goodness of fit to quantify network performance [ 20 , 21 ]. In addition, we also use some traditional machine learning methods to compare with the deep neural network model we designed, to demonstrate the advantages of deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Zhao

Wang

et al. 2023

Micromachines

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Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 102 and 1.38 × 103 times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices.

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Section: Introductionmentioning

confidence: 99%

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Zhao

Wang

et al. 2023

Micromachines

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“…Three-channel EMG identifying hand gestures, reprinted from, [82] copyright(2020), Kwon et al with permission from Springer Nature (Middle, fourth). Machine learning accuracy matrix, reprinted from, [120] hyperbolic tangent, [10] and the popular rectified linear unit (ReLU), [11] which is the identity function for values greater than zero and zero otherwise. This approach of learned linear transformation followed by nonlinear activation has been known since at least the 1980s to provide neural networks of sufficient complexity the ability to learn any function, [12] and this expressivity has yielded much success in biosignal processing tasks as this review details.…”

Section: Introductionmentioning

confidence: 99%

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

2022

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Analysis & Sensing www.analysis-sensing.org Review doi.org/10.1002/anse.202200062Wearable devices have received significant attention recently for their ability to monitor critical physiological signals noninvasively, such as electrocardiography, electroencephalography, electromyography, and photoplethysmography. These biointegrated wearable systems can potentially fill gaps in conventional clinical practice by providing highly cost-effective health characterization and portable continuous health monitoring. Further, the physiological signals measured by wearables require post-processing to derive meaningful values, such as heart rate or blood oxygen saturation. This requirement, in conjunction with the smaller form factor and limited sensor count of the miniaturized systems, often necessitates robust signal processing and data analysis to approach the stringent performance specifications of conventional medical devices, and machine learning techniques have found success in filling this analytical role for their ability to learn complex functional relationships. Thus, this review outlines a systematic summary of the latest research on various wearable devices and their biosignal sensing and signal processing methods, emphasizing machine learning. We also discuss the developmental challenges and advantages of current machine-learning methods, while suggesting research directions for future studies.

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“…It can be seen that CNN and DNN fully support the prediction research of devices [15]. At the same time, we use accuracy, mean square error, goodness of fit (R 2 ) to quantitatively analyze the prediction results [18,19]. This method has high speed, high accuracy and can be used to study the SEE without further study of relevant device physics knowledge.…”

Section: Introductionmentioning

confidence: 99%

Single event effects prediction of MOSFET device using deep learning

Zhao¹,

Wang²,

Duan³

et al. 2022

Nanotechnology

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Single event effect(SEE) is an important problem in the reliability research of integrated circuits. The study of SEE of traditional MOSFET devices is mainly based on simulation software, which is characterized by slow simulation speed, large computation and time-consuming. In this paper, a single event effect research method based on deep learning (DL) is proposed. The method relies on 28-nm MOSFET. The complete drain transient current pulse, transient current peak value and total collected charge can be obtained in a short time by inputting relevant parameters that affect the SEE. The accuracy of the network for predicting transient current peak and total collected charge is 96.95% and 97.53% respectively, and the mean goodness of fit of the network for predicting the drain transient current pulse curve is 0.985. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.89×103 and 1.50×103 times respectively. This method has good prediction effect and provides a new possibility for the study of single event effect.

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Biosignal Classification and Disease Prediction with Deep Learning

Cited by 6 publications

References 2 publications

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

Single event effects prediction of MOSFET device using deep learning

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